Date of Award
Doctor of Philosophy (PhD)
Articular cartilage covers the ends of bones, providing a protective layer that can endure forces during movement. Chondrocytes, the only cell type found in articular cartilage, produce collagen fibers and proteoglycans that maintain the extracellular matrix (ECM). Cartilage damage impairs chondrocytes and their ability to maintain tissue stability. There are various factors that can cause cell death, which can be broadly classified into two categories: programmed cell death (apoptosis) and non-programmed cell death (necrosis). The consequences of cell death can have significant impacts on the extracellular matrix (ECM). The ECM is a complex network of proteins and carbohydrates that provide structural and biochemical support to the surrounding cells. Studies have shown that changes in chondrocyte viability (CV) are linked to cartilage damage. Assessing CV is crucial in determining cartilage injury or degenerative joint diseases. Traditional dye-labeled methods are invasive and unsuitable for assessing CV in vivo or over an extended period. Non-invasive techniques that provide real-time results without the need for labeling are highly necessary. Previous studies conducted by our research team have shown that the autofluorescence intensity of chondrocytes acquired by the label-free imaging method can be used as a reliable indicator to differentiate between live and dead cells. Specifically, the intensity of the autofluorescence emitted by live chondrocytes is significantly different than that of dead chondrocytes. This information is useful for researchers who are studying cartilage and looking to distinguish between live and dead chondrocytes in their experiments.
This dissertation aims to improve the instrumentation and image analysis tools for the label-free CV assessment and to explore the correlation between mechanical properties and CV. Firstly, we built a customized desktop two-photon and SHG microscope to take high-resolution label-free images of chondrocyte and ECM structures in articular cartilage. We also developed a Group Velocity Dispersion (GVD) compensation system to improve the two-photon excitation efficiency and enhance the signal to noise ratio. To achieve accurate and efficient CV measurement, we developed an automated imaging analysis algorithm based on a deep-learning algorithm, Mask R-CNN, which uses label-free images to quantify CV in two dimensions (2D). To gain more information about chondrocytes and ECM in three dimensions (3D), we developed a 3D imaging analysis method based on our 2D algorithm. Additionally, we used label-free imaging methods to assess CV in 3D and performed mechanical testing to measure permeability and equilibrium modulus on the same cartilage samples. Our initial experiments indicated that 3D CV has a positive correlation with permeability and a negative correlation with equilibrium modulus.
Label-free imaging combined with deep learning imaging analysis methods holds the potential to enhance our comprehension of joint diseases, enable early diagnosis, and provide insights into the response of chondrocytes to mechanical or biochemical stimuli. In the foreseeable future, this method can be utilized in clinical settings to evaluate the health of cartilage tissue in patients.
Fan, Hongming, "Assessing Chondrocyte Viability of Articular Cartilage Using Label-Free Imaging Methods" (2023). All Dissertations. 3461.
Available for download on Tuesday, December 31, 2024